Image registration is an important task in applications such as image quality assessment, medical imaging, automatic target recognition, and so forth. Existing image registration methods include for example: a paper by A. Averbuch, R. R. Coifman, D. L. Donoho, M. Israeli, Y. Shkolnisky, and I. Sedelnikov, “A Framework for Discrete Integral Transformations II—The 2D Discrete Radon Transform,” SIAM Journal on Scientific Computing, vol. 30, no. 2, pp. 785-803, January 2008, who have used pseudo-polar-based estimation of large translations, rotations, and scalings in images; a paper by G. Wolberg and S. Zokai “Robust Image Registration Using Log-Polar Transform,” Proceedings of the IEEE International Conference on Image Processing (ICIP), pp. 493-496, 2000, who have also worked on robust image registration by using the log-polar transform paper by G. Varghese and Z. Wang, “Video denoising based on a spatiotemporal Gaussian scale mixture model,” IEEE Transactions on Circuits and Systems for Video Technology, vol. 20, no. 7, pp. 1032-1040, 2010, who have used the Fourier transform to estimate global spatial shifts; a paper by B. S. Reddy and B. N. Chatterji, “An FFT-based technique for translation, rotation and scale-invariant image registration,” IEEE Transactions on Image Processing, vol. 5, no. 8, pp. 1266-1271, 1996 who have proposed an FFT-based technique for translation, rotation and scale-invariant image registration; a paper by K. Jafari-Khouzani and H. Soltanian-Zadeh, “Radon transform orientation estimation for rotation invariant texture analysis,” IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 27, no. 6, pp. 1004-1008, 2005 who have applied the Radon transform to orientation estimation for rotation invariant texture analysis; a paper by E. De Castro and C. Morandi, “Registration of translated and rotated images using finite Fourier transforms,” IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. PAMI-95, pp. 700-703, 1987 who have developed an image registration method for translated and rotated images using finite Fourier transforms, but this method fails in the presence of scale change; a paper by W. Wei, S. Wang, X. Zhang and Z. Tang, “Estimation of image rotation angle using interpolation-related spectral signatures with application to blind detection of image forgery,” IEEE Transactions on Information Forensics and Security, vol. 5, no. 3, pp. 507-517, 2010 who have estimated the image rotation angle by using interpolation-related spectral signatures with application to blind detection of image forgery; a paper by D. G. Lowe, “Distinctive Image Features from Scale-Invariant Keypoints,” International Journal of Computer Vision, vol. 60, no. 2, pp. 91-110, 2004 who has proposed a distinctive image feature approach for scale-invariant keypoint extraction and registration (SIFT); and a paper by H. Bay, A. Ess, T. Tuytelaars and L. Van Gool “SURF: Speeded Up Robust Features,” Computer Vision and Image Understanding (CVIU), vol. 110, no. 3, pp. 346-359, 2008 who have developed SURF (Speeded Up Robust Feature), which was claimed to be faster than SIFT.
In full reference image quality assessment, a comparison of a distorted image against a reference image is often used to obtain a visual quality metric, which is computed by comparing the two images. A number of methods exist for generating such a quality metric, among these: a simple peak signal-to-noise ratio (PSNR) measurement, a structural similarity (SSIM) index proposed in Z. Wang, A. C. Bovik, H. R. Sheikh and E. P. Simoncelli, “Image quality assessment: From error visibility to structural similarity,” IEEE Transactions on Image Processing, vol. 13, no. 4, pp. 600-612, 2004, a visual information fidelity (VIF) index that was proposed in H. R. Sheikh and A. C. Bovik, “Image information and visual quality.” IEEE Transactions on Image Processing, vol. 15, no. 2, pp. 430-444, 2006. Further, Rezazadeh and Coulombe proposed a discrete wavelet transform framework for full-reference image quality assessment in “Novel discrete wavelet transform framework for full reference image quality assessment,” Signal, Image and Video Processing, pp. 1-15, September 2011, and in “A novel discrete wavelet domain error-based image quality metric with enhanced perceptual performance,” to appear in Procedia Engineering (Elsevier), while Qian and Chen in “Four reduced-reference metrics for measuring hyperspectral images after spatial resolution enhancement,” ISPRS International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, Vienna, Austria, pp. 204-208, Jul. 5-7, 2010 developed four reduced-reference metrics for measuring the visual quality of hyperspectral images after spatial resolution enhancement.
However, there is still the need in the industry for the development of simple yet quick and accurate methods for realignment of an image with a reference image, which would avoid or mitigate disadvantages of the prior art, be capable of improving the objective measurement of visual image quality, and provide image registration that is robust to noise.